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Record W4387379159 · doi:10.1021/acs.jctc.3c00547

Martini 3 Coarse-Grained Force Field for Cholesterol

2023· article· en· W4387379159 on OpenAlexafffund
Luís Borges-Araújo, Ana C. Borges-Araújo, Tuǧba N. Öztürk, Daniel P. Ramirez-Echemendía, Balázs Fábián, Timothy S. Carpenter, Sebastian Thallmair, Jonathan Barnoud, Helgi I. Ingólfsson, Gerhard Hummer, D. Peter Tieleman, ‪Siewert J. Marrink, Paulo C. T. Souza, Manuel N. Melo

Bibliographic record

VenueJournal of Chemical Theory and Computation · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicLipid Membrane Structure and Behavior
Canadian institutionsUniversity of Calgary
FundersLawrence Livermore National LaboratoryLaboratory Directed Research and DevelopmentNational Supercomputing Center, Korea Institute of Science and Technology InformationFundação para a Ciência e a TecnologiaEuropean Research CouncilDr. Rolf M. Schwiete StiftungUniversidade de Santiago de CompostelaAlfons und Gertrud Kassel-StiftungAlliance de recherche numérique du CanadaCentre National de la Recherche ScientifiqueMax-Planck-GesellschaftUniversidade Nova de LisboaHessisches Ministerium für Wissenschaft und KunstDirectorate for Biological SciencesCanada Research ChairsRijksuniversiteit GroningenUniversity of BristolInstituto de Tecnologia Química e Biológica, Universidade Nova de LisboaNatural Sciences and Engineering Research Council of CanadaGrand Équipement National De Calcul IntensifUniversité de LyonAlexander von Humboldt-StiftungU.S. Department of Energy
KeywordsRigidity (electromagnetism)Force field (fiction)CholesterolComputer scienceForce balanceNanotechnologyPermeability (electromagnetism)ChemistryMaterials scienceMembraneMechanicsComposite materialPhysicsArtificial intelligenceBiochemistry

Abstract

fetched live from OpenAlex

Cholesterol plays a crucial role in biomembranes by regulating various properties, such as fluidity, rigidity, permeability, and organization of lipid bilayers. The latest version of the Martini model, Martini 3, offers significant improvements in interaction balance, molecular packing, and inclusion of new bead types and sizes. However, the release of the new model resulted in the need to reparameterize many core molecules, including cholesterol. Here, we describe the development and validation of a Martini 3 cholesterol model, addressing issues related to its bonded setup, shape, volume, and hydrophobicity. The proposed model mitigates some limitations of its Martini 2 predecessor while maintaining or improving the overall behavior.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.020
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0110.002

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.011
GPT teacher head0.290
Teacher spread0.279 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations80
Published2023
Admission routes2
Has abstractyes

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